pythonProject/diffusers-main/docs/source/en/using-diffusers/reusing_seeds.md

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# Reproducibility

Diffusion is a random process that generates a different output every time. For certain situations like testing and replicating results, you want to generate the same result each time, across releases and platforms within a certain tolerance range.

This guide will show you how to control sources of randomness and enable deterministic algorithms.

## Generator

Pipelines rely on [torch.randn](https://pytorch.org/docs/stable/generated/torch.randn.html), which uses a different random seed each time, to create the initial noisy tensors. To generate the same output on a CPU or GPU, use a [Generator](https://docs.pytorch.org/docs/stable/generated/torch.Generator.html) to manage how random values are generated.

> [!TIP]
> If reproducibility is important to your use case, we recommend always using a CPU `Generator`. The performance loss is often negligible and you'll generate more similar values.

<hfoptions id="generator">
<hfoption id="GPU">

The GPU uses a different random number generator than the CPU. Diffusers solves this issue with the [`~utils.torch_utils.randn_tensor`] function to create the random tensor on a CPU and then moving it to the GPU. This function is used everywhere inside the pipeline and you don't need to explicitly call it.

Use [manual_seed](https://docs.pytorch.org/docs/stable/generated/torch.manual_seed.html) as shown below to set a seed.

```py
import torch
import numpy as np
from diffusers import DDIMPipeline

ddim = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32", device_map="cuda")
generator = torch.manual_seed(0)
image = ddim(num_inference_steps=2, output_type="np", generator=generator).images
print(np.abs(image).sum())
```

</hfoption>
<hfoption id="CPU">

Set `device="cpu"` in the `Generator` and use [manual_seed](https://docs.pytorch.org/docs/stable/generated/torch.manual_seed.html) to set a seed for generating random numbers.

```py
import torch
import numpy as np
from diffusers import DDIMPipeline

ddim = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
generator = torch.Generator(device="cpu").manual_seed(0)
image = ddim(num_inference_steps=2, output_type="np", generator=generator).images
print(np.abs(image).sum())
```

</hfoption>
</hfoptions>

The `Generator` object should be passed to the pipeline instead of an integer seed. `Generator` maintains a *random state* that is consumed and modified when used. Once consumed, the same `Generator` object produces different results in subsequent calls, even across different pipelines, because it's *state* has changed.

```py
generator = torch.manual_seed(0)

for _ in range(5):
-    image = pipeline(prompt, generator=generator)
+    image = pipeline(prompt, generator=torch.manual_seed(0))
```

## Deterministic algorithms

PyTorch supports [deterministic algorithms](https://docs.pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms) - where available - for certain operations so they produce the same results. Deterministic algorithms may be slower and decrease performance.

Use Diffusers' [enable_full_determinism](https://github.com/huggingface/diffusers/blob/142f353e1c638ff1d20bd798402b68f72c1ebbdd/src/diffusers/utils/testing_utils.py#L861) function to enable deterministic algorithms.

```py
import torch
from diffusers_utils import enable_full_determinism

enable_full_determinism()
```

Under the hood, `enable_full_determinism` works by:

- Setting the environment variable [CUBLAS_WORKSPACE_CONFIG](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility) to `:16:8` to only use one buffer size during rntime. Non-deterministic behavior occurs when operations are used in more than one CUDA stream.
- Disabling benchmarking to find the fastest convolution operation by setting `torch.backends.cudnn.benchmark=False`. Non-deterministic behavior occurs because the benchmark may select different algorithms each time depending on hardware or benchmarking noise.
- Disabling TensorFloat32 (TF32) operations in favor of more precise and consistent full-precision operations.


## Resources

We strongly recommend reading PyTorch's developer notes about [Reproducibility](https://docs.pytorch.org/docs/stable/notes/randomness.html). You can try to limit randomness, but it is not *guaranteed* even with an identical seed.